Kd data generated based Semi-Analytical Algorithm developed by Maciel et al. (2020) (https://doi.org/10.1016/j.isprsjprs.2020.10.009). The underwater light field modeling is essential for the understanding of biogeochemical processes, such as photosynthesis, carbon fluxes, and sediment transports in inland waters. Water-column light attenuation can be quantified by the diffuse attenuation coefficient of the downwelling irradiance (Kd). This dataset represents the Kd estimate for a Sentinel-2/MSI time-series at Curuai Lake region - Lower Amazon floodplains. This time-series data was generated for 66 Sentinel-2/MSI scenes (08/2015 to 09/2019) during the research paper titled Mapping of diffuse attenuation coefficient in optically complex waters of amazon floodplain lakes. This product was funded by the Brazilian Development Bank (BNDES), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

AMAZONIA-1/WFI - Level-4 Surface Reflectance product. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG).

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.

This land cover classification refers to a study area in Mato Grosso state, in the Cerrado biome. For this map, the CBERS-4/WFI monthly data cube was used, with a spatial resolution of 64 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the CBERS-4/WFI data cube. The input datacube was CBERS-4 (WFI) Cube Identity - v001, which was deprecated. The classification was made using 852 samples (Annual Crop: 257; Natural Vegetation: 245; Pasture: 216; Semi-Perennial Crop: 134) and the following data cube bands: bands red, green, blue, and near-infrared along with the EVI, NDVI, GEMI, GNDVI, NDWI2, PVR indices. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package. This product was funded by the Brazilian Development Bank (BNDES).

This is a land cover classification map of Brazilian Amazon, from January to December of 2018. This classification was made on top of Landsat-8 biweekly cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 2303 sample points (Agriculture: 405, Forest: 1284, Pasture: 482). The spectral band used were B1, B2, B3, B4, B5, B6, and B7 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. The classification algorithm was Random Forest. The post-processing included masking water (using Pekel et al 2016). This product was funded by the Brazilian Development Bank (BNDES).

This is a land cover classification map of Brazilian Cerrado, from August 29th 2017 to August 29th 2018. This classification was made on top of Landsat-8 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated. The classification model was trained using 48850 sample points spread across the Cerrado biome (Annual Crop 6887, Cerradao 4211, Cerrado 16251, Natural Non Vegetation 38, Open_Cerrado 5658, Pasture 12894, Perennial Crop 68, Silviculture 805, Sugarcane 1775, Water 263). The spectral band used were B1, B2, B3, B4, B5, B6, and B7 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. The classification algorithm was TempCNN (Deep Learning). This product was funded by the Brazilian Development Bank (BNDES).

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types.